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Sentry.io VS Apache Airflow

Compare Sentry.io VS Apache Airflow and see what are their differences

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  3. » Reviews
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Sentry.io logo Sentry.io

From error tracking to performance monitoring, developers can see what actually matters, solve quicker, and learn continuously about their applications - from the frontend to the backend.

Apache Airflow logo Apache Airflow

Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
  • Sentry.io Landing page
    Landing page //
    2023-08-26
  • Apache Airflow Landing page
    Landing page //
    2023-06-17

Sentry.io features and specs

  • Real-time error tracking
    Sentry provides real-time error tracking, ensuring that developers are immediately notified of errors as they occur. This allows for faster debugging and reduces downtime.
  • Detailed error reports
    Sentry generates detailed error reports which include stack traces, diagnostic data, and contextual information, making it easier to understand and resolve issues.
  • Integrations
    Sentry integrates seamlessly with a wide range of development tools and services such as GitHub, Slack, Jira, and more, allowing for smooth workflows and streamlined issue management.
  • Releases and version tracking
    Sentry's releases feature allows developers to track errors and performance issues specific to software releases, helping in identifying regressions and ensuring each new version is more stable.
  • Performance monitoring
    Beyond error tracking, Sentry offers performance monitoring which helps in identifying slow performance issues and bottlenecks within the application.
  • User feedback
    Sentry allows capturing user feedback directly within the application, which can provide additional context to errors and improve the overall user experience.

Possible disadvantages of Sentry.io

  • Pricing
    Sentry's pricing model can be expensive for small teams or startups, especially if they need advanced features or higher usage limits.
  • Complexity
    Despite its rich feature set, Sentry can be quite complex to configure and use, particularly for developers who are new to error tracking and monitoring tools.
  • Learning curve
    There is a learning curve associated with Sentry, both in terms of setup and effectively utilizing all its features to their full potential.
  • Potential privacy concerns
    Given that Sentry collects a significant amount of diagnostic data, there may be privacy concerns, especially in regulated industries that require strict data compliance.
  • Resource usage
    The integration of Sentry into an application can add some overhead in terms of resource usage, which might be a concern for high-performance applications.

Apache Airflow features and specs

  • Scalability
    Apache Airflow can scale horizontally, allowing it to handle large volumes of tasks and workflows by distributing the workload across multiple worker nodes.
  • Extensibility
    It supports custom plugins and operators, making it highly customizable to fit various use cases. Users can define their own tasks, sensors, and hooks.
  • Visualization
    Airflow provides an intuitive web interface for monitoring and managing workflows. The interface allows users to visualize DAGs, track task statuses, and debug failures.
  • Flexibility
    Workflows are defined using Python code, which offers a high degree of flexibility and programmatic control over the tasks and their dependencies.
  • Integrations
    Airflow has built-in integrations with a wide range of tools and services such as AWS, Google Cloud, and Apache Hadoop, making it easier to connect to external systems.

Possible disadvantages of Apache Airflow

  • Complexity
    Setting up and configuring Apache Airflow can be complex, particularly for new users. It requires careful management of infrastructure components like databases and web servers.
  • Resource Intensive
    Airflow can be resource-heavy in terms of both memory and CPU usage, especially when dealing with a large number of tasks and DAGs.
  • Learning Curve
    The learning curve can be steep for users who are not familiar with Python or the underlying concepts of workflow management.
  • Limited Real-Time Processing
    Airflow is better suited for batch processing and scheduled tasks rather than real-time event-based processing.
  • Dependency Management
    Managing task dependencies in complex DAGs can become cumbersome and may lead to configuration errors if not properly handled.

Sentry.io videos

+ Add

Application Monitoring 101: Getting Started with Sentry

Apache Airflow videos

+ Add

Airflow Tutorial for Beginners - Full Course in 2 Hours 2022

Category Popularity

0-100% (relative to Sentry.io and Apache Airflow)

Sentry.io

Apache Airflow

Error Tracking
100 100%
Error Tracking
0% 0
Workflow Automation
0 0%
Workflow Automation
100% 100
Monitoring Tools
100 100%
Monitoring Tools
0% 0
Automation
0 0%
Automation
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Sentry.io and Apache Airflow

Sentry.io Reviews

Comparison of Cron Monitoring Services (November 2023)
Sentry launched in 2012, is registered in the United States and runs on AWS and Google Cloud. Sentry is a VC-funded company and has 200+ employees. Sentry started as an error tracking service, grew into APM, and launched cron monitoring support in public beta in January 2023. Sentry uses the SaaS business model, but its source code is available under the FSL license. Sentry...
Source: blog.healthchecks.io
5 Best DevSecOps Tools in 2023
There are many platforms that can be utilized for monitoring and alerting. Some examples are New Relic, Datadog, AWS CloudWatch, Sentry, Dynatrace, and others. Again, these providers each have pros and cons related to pricing, offering, ad vendor lock-in. So research the options to see what may possibly be best for a given situation.
Source: devopsauthority.tech
13 tools to use for DevSecOps automation
💰 Sentry.io is a service that helps you monitor and fix crashes in real-time, so that you can diagnose and optimize code performance. The Sentry.io node allows you to manage information about events, issues, projects, and releases.
Source: n8n.io
Best Error Monitoring Services for Elixir Phoenix
Sentry provides an Elixir-specific getting started guide to walk you through setup. It also provides an Elixir SDK you can add as a mix.exs package. Sentry limits email support to only customers on certain plans. However, it does offer a community forum to ask questions.
Source: staknine.com
6 Bugsnag Alternatives to Consider in 2021
Sentry is a cloud-hosted error tracking tool that helps to resolve crashes and other similar issues in your apps. Many software teams use Sentry to enhance their deployed app’s efficiency and build a better user experience. Sentry assists you in catching and fixing multiple errors together with ease. In general, this error tracking solution can automatically track all types...
Source: scoutapm.com

Apache Airflow Reviews

5 Airflow Alternatives for Data Orchestration
While Apache Airflow continues to be a popular tool for data orchestration, the alternatives presented here offer a range of features and benefits that may better suit certain projects or team preferences. Whether you prioritize simplicity, code-centric design, or the integration of machine learning workflows, there is likely an alternative that meets your needs. By...
Source: www.kdnuggets.com
Top 8 Apache Airflow Alternatives in 2024
Apache Airflow is a workflow streamlining solution aiming at accelerating routine procedures. This article provides a detailed description of Apache Airflow as one of the most popular automation solutions. It also presents and compares alternatives to Airflow, their characteristic features, and recommended application areas. Based on that, each business could decide which...
Source: blog.skyvia.com
10 Best Airflow Alternatives for 2024
In a nutshell, you gained a basic understanding of Apache Airflow and its powerful features. On the other hand, you understood some of the limitations and disadvantages of Apache Airflow. Hence, this article helped you explore the best Apache Airflow Alternatives available in the market. So, you can try hands-on on these Airflow Alternatives and select the best according to...
Source: hevodata.com
A List of The 16 Best ETL Tools And Why To Choose Them
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
Source: www.datacamp.com
15 Best ETL Tools in 2022 (A Complete Updated List)
Apache Airflow programmatically creates, schedules and monitors workflows. It can also modify the scheduler to run the jobs as and when required.
Source: www.softwaretestinghelp.com

Social recommendations and mentions

Apache Airflow might be a bit more popular than Sentry.io. We know about 75 links to it since March 2021 and only 67 links to Sentry.io. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Sentry.io mentions (67)

  • 5 Essential Tools Every Bootstrapped SaaS Startup Needs to Succeed
    Sentry is a powerful error monitoring and performance tracking tool designed for modern SaaS applications. - Source: dev.to / 3 months ago
  • How to Fix an Error within Minutes with Sentry and GitAuto
    Our Sentry dashboard shows a TypeError with the message 'NoneType' object is not iterable. The error occurs in:. - Source: dev.to / 3 months ago
  • The Risks of User Impersonation
    The next rung up are User recordings. For users that are having issues, we have concrete recorded data for their flow. The flows would include anything relevant to the application, how they used it, what actions they took. All so we can actually see what happened in context for when there is a problem. No one wants to spend any time looking at recordings if they don't have to. It is also very difficult to identify... - Source: dev.to / 4 months ago
  • This Month in Solid #10: SolidHack 2024 Winners 😎
    We also want to share a huge thank you to our sponsors Netlify and Sentry. - Source: dev.to / 6 months ago
  • How to Define Engineering Standards (with Backstage)
    Using the example of AcmeCorp.com again, let’s take one of their areas are turn it into a Scorecard with a series of checks. They use Datadog for their dashboards and Sentry for their logging so they can both provide sources of truth for their checks. - Source: dev.to / 8 months ago
View more

Apache Airflow mentions (75)

  • The DOJ Still Wants Google to Sell Off Chrome
    Is this really true? Something that can be supported by clear evidence? I’ve seen this trotted out many times, but it seems like there are interesting Apache projects: https://airflow.apache.org/ https://iceberg.apache.org/ https://kafka.apache.org/ https://superset.apache.org/. - Source: Hacker News / 2 months ago
  • 10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
    Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools. - Source: dev.to / 3 months ago
  • Data Orchestration Tool Analysis: Airflow, Dagster, Flyte
    Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring... - Source: dev.to / 4 months ago
  • AIOps, DevOps, MLOps, LLMOps – What’s the Difference?
    Data pipelines: Apache Kafka and Airflow are often used for building data pipelines that can continuously feed data to models in production. - Source: dev.to / 4 months ago
  • Data Engineering with DLT and REST
    This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``. - Source: dev.to / 6 months ago
View more

What are some alternatives?

When comparing Sentry.io and Apache Airflow, you can also consider the following products

Raygun - Raygun gives developers meaningful insights into problems affecting their applications. Discover issues - Understand the problem - Fix things faster.

Make.com - Tool for workflow automation (Former Integromat)

Rollbar - Rollbar collects errors that happen in your application, notifies you, and analyzes them so you can debug and fix them. Ruby, Python, PHP, Node.js, JavaScript, and Flash libraries available.

ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.

Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.

Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.

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